Carbon instensity of Sustainable PV for Energy Transition¶

This analysis explores the carbon implications of different PV sustainability/circular economy designs in the context of achieving energy transition. These calculations build upon previous work that can be found in journals 13 and 17.

Attempt 1

In [1]:
import numpy as np
import pandas as pd
import os,sys
from pathlib import Path
import matplotlib.pyplot as plt

cwd = os.getcwd() #grabs current working directory

testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'CarbonAnalysis')
inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')
baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')
supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
carbonfolder = str(Path().resolve().parent.parent / 'PV_ICE'/ 'baselines'/ 'CarbonLayer')
altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')
energyanalysisfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'EnergyAnalysis')

if not os.path.exists(testfolder):
    os.makedirs(testfolder)
In [2]:
from platform import python_version 
print(python_version())

3.11.5
In [3]:
import PV_ICE
PV_ICE.__version__
Out[3]:
''
In [4]:
#https://www.learnui.design/tools/data-color-picker.html#palette
#color pallette - modify here for all graphs below
colorpalette=['#000000', #PV ICE baseline
              '#595959', '#7F7F7F', '#A6A6A6', '#D9D9D9', #BAU, 4 grays, perc, shj, topcon, irena
              #'#067872','#0aa39e','#09d0cd','#00ffff', #realistic cases (4) teals, perc, shj, topcon, irena
              '#0579C1','#C00000','#FFC000', #extreme cases (3) long life, high eff, circular
                '#6E30A0','#00B3B5','#10C483', #ambitious modules (5) high eff+ long life, 50 yr perc, recycleSi, 
               '#97CB3F','#FF7E00' #circular perovskite+life, circular perovkiste+ high eff
                ] 

colormats = ['#00bfbf','#ff7f0e','#1f77be','#2ca02c','#d62728','#9467BD','#8C564B', 'black'] #colors for material plots       

import matplotlib as mpl #import matplotlib
from cycler import cycler #import cycler
mpl.rcParams['axes.prop_cycle'] = cycler(color=colorpalette) #reset the default color palette of mpl

from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)

plt.rcParams.update({'font.size': 14})
plt.rcParams['figure.figsize'] = (8, 6)

scennames_labels = ['PV_ICE','PERC','SHJ','TOPCon','Low\nQuality',
                         'Long-Lived','High Eff','Circular',
                        'High Eff\n+ Long-life','Long-Life\n+ Recycling',
                         'Recycled-Si\n+ Long-life','Circular\n+ Long-life',
                        'Circular\n+ High Eff'
                    ]  

scennames_labels_flat = ['PV_ICE','PERC','SHJ','TOPCon','Low Quality',
                         'Long-Lived','High Eff','Circular',
                        'High Eff + Long-life','Long-Life + Recycling',
                         'Recycled-Si + Long-life','Circular + Long-life',
                        'Circular + High Eff'
                    ]
In [5]:
MATERIALS = ['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet']
moduleFile_m = os.path.join(baselinesfolder, 'baseline_modules_mass_US.csv')
moduleFile_e = os.path.join(baselinesfolder, 'baseline_modules_energy.csv')
In [ ]:
In [198]:
#load in the simulation from Energy Analysis journal
#sim1 = PV_ICE.Simulation.load_Simpickle(filename=r'C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis\Deploy_MirletzLinear\sim1.pkl')
sim1 = PV_ICE.Simulation.load_Simpickle(filename=r'C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis\Deploy_HieslmairSigmoid\sim1.pkl')
#sim1 = PV_ICE.Simulation.load_Simpickle(filename=r'C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis\Deploy_ExpoToFlat\sim1.pkl')
In [199]:
sim1.scenario['r_PERC'].dataOut_e
Out[199]:
mod_MFG mod_Install mod_OandM mod_Repair mod_Demount mod_Store mod_Resell_Certify mod_ReMFG_Disassembly mod_Recycle_Crush e_out_annual_[Wh]
0 5.100675e+10 5.218383e+07 0.0 0.0 0.000000e+00 0.0 0.0 0.0 0.000000e+00 1.256856e+12
1 2.000736e+10 2.024658e+07 0.0 0.0 3.334110e-01 0.0 0.0 0.0 1.410855e+00 1.741612e+12
2 2.276802e+10 2.279250e+07 0.0 0.0 2.577242e+01 0.0 0.0 0.0 1.090580e+02 2.289873e+12
3 3.488451e+10 3.455051e+07 0.0 0.0 3.160848e+02 0.0 0.0 0.0 1.337538e+03 3.131165e+12
4 7.146438e+10 7.003510e+07 0.0 0.0 1.821996e+03 0.0 0.0 0.0 7.709921e+03 4.863158e+12
... ... ... ... ... ... ... ... ... ... ...
96 2.679303e+13 7.513347e+10 0.0 0.0 6.263916e+10 0.0 0.0 0.0 3.147618e+12 1.355841e+17
97 2.893271e+13 8.113359e+10 0.0 0.0 6.984759e+10 0.0 0.0 0.0 3.509841e+12 1.359336e+17
98 1.561072e+13 4.377583e+10 0.0 0.0 2.459524e+10 0.0 0.0 0.0 1.235911e+12 1.362831e+17
99 1.586147e+13 4.447900e+10 0.0 0.0 2.538845e+10 0.0 0.0 0.0 1.275769e+12 1.366325e+17
100 1.620853e+13 4.545222e+10 0.0 0.0 2.650713e+10 0.0 0.0 0.0 1.331983e+12 1.369820e+17

101 rows × 10 columns

In [200]:
sim1.scenario['PV_ICE'].dataOut_m['Effective_Capacity_[W]'].plot()
Out[200]:
<Axes: >

Project grid forward to 100% re in 2050¶

To parallel the PV deployment, we will assume that we globally hit 100% RE in 2050 with the 75 TW of PV. As such, we need to change the future projection of marketshares of the different country grids.

One scenario with decarb grid, one scenario with decarb grid and heat

Estimating that 60-70% generation will be from Solar, 30-40% from wind, and any remainder from "other renewables"

In [201]:
countrygridmix = pd.read_csv(os.path.join(carbonfolder,'baseline_countrygridmix.csv'), index_col='year')
gridsources = ['Bioenergy','Hydro','Nuclear','OtherFossil','OtherRenewables','Solar','Wind']
nonRE = ['Coal','Gas','OtherFossil','Nuclear','Bioenergy']
In [202]:
countrygridmix.loc[2023:,:]=np.nan #delete 2023 to 2050
nonRE_search = '|'.join(nonRE) #create nonRE search
countrygridmix.loc[2050, countrygridmix.columns.str.contains(nonRE_search)] = 0.0 #set all nonRE to 0 in 2050
In [203]:
countrygridmix.loc[2050, countrygridmix.columns.str.contains('Solar')] = 63.0
countrygridmix.loc[2050, countrygridmix.columns.str.contains('Wind')] = 33.0
countrygridmix.loc[2050, countrygridmix.columns.str.contains('Hydro')] = 3.0
countrygridmix.loc[2050, countrygridmix.columns.str.contains('OtherRenewables')] = 1.0
#numbers derived from leading scenario electricity generation Breyer et al 2022 scenarios (EU focused)
In [204]:
countrygridmix_100RE2050 = countrygridmix.interpolate() #linearly interpolate between 2022 and 2050
In [205]:
apnd_idx = pd.RangeIndex(start=2051,stop=2101,step=1) #create temp df
apnd_df = pd.DataFrame(columns=countrygridmix_100RE2050.columns, index=apnd_idx )
countrygridmix_100RE20502100 = pd.concat([countrygridmix_100RE2050.loc[2000:],apnd_df], axis=0) #extend through 2100
countrygridmix_100RE20502100.ffill(inplace=True) #propogate 2050 values through 2100
In [206]:
countrygridmix_100RE20502100.loc[2050]
Out[206]:
China_Bioenergy            0.0
China_Coal                 0.0
China_Gas                  0.0
China_Hydro                3.0
China_Nuclear              0.0
                          ... 
Zambia_Nuclear             0.0
Zambia_OtherFossil         0.0
Zambia_OtherRenewables     1.0
Zambia_Solar              63.0
Zambia_Wind               33.0
Name: 2050, Length: 472, dtype: float64

This is a simple projection, assumes all countries have same ratio of PV and wind (which we know can't be true). Update in future with country specific projections.

In [207]:
pd.read_csv(os.path.join(carbonfolder,'baseline_electricityemissionfactors.csv'), index_col=[0])
Out[207]:
CO2eq_gpWh_IPCC2006 CO2eq_gpWh_ember CO2_gpWh_EIA CO2_gpWh_EPA
Energy Source
Bioenergy 0.3005 0.230 0.0000 0.3170
Coal 0.3487 0.820 0.3215 0.3380
Gas 0.2291 0.490 0.1805 0.1810
Hydro 0.0000 0.024 0.0000 0.0000
Nuclear 0.0000 0.012 0.0000 0.0000
OtherFossil 0.2671 0.700 0.2413 0.0000
OtherRenewables 0.0000 0.038 0.0000 0.0000
Solar 0.0000 0.048 0.0000 0.0000
Wind 0.0000 0.011 0.0000 0.0000
SteamAndHeat 0.0000 0.000 0.0000 0.2266
In [208]:
sim1.calculateCarbonFlows(countrygridmixes=countrygridmix_100RE20502100)


>>>> Calculating Carbon Flows <<<<

Working on Scenario:  PV_ICE
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_PERC
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_SHJ
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_TOPCon
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_IRENA
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  ex_Life
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  ex_High_eff
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  ex_Circular
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_EffLife
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_50PERC
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_RecycledPERC
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_Perovskite_life
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_Perovskite_Eff
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
In [ ]:

Carbon Analysis¶

this will become the aggregate carbon results function

In [209]:
scenarios = sim1.scenario
In [210]:
#simply group mod and mats carbon dfs by scenario
sim_carbon_dfs = pd.DataFrame()

for scen in scenarios:
    print(scen)
    mod_carbon_scen_results = sim1.scenario[scen].dataOut_c.add_prefix(str(scen+'_'))
    
    scenmatdc = pd.DataFrame()
    for mat in MATERIALS:
        print(mat)
        mat_carbon_scen_results = sim1.scenario[scen].material[mat].matdataOut_c.add_prefix(str(scen+'_'+mat+'_')) 
        scenmatdc = pd.concat([scenmatdc,mat_carbon_scen_results], axis=1) #group all material dc
    
    scen_carbon_results = pd.concat([mod_carbon_scen_results,scenmatdc], axis=1) #append mats to mod
    sim_carbon_dfs = pd.concat([sim_carbon_dfs, scen_carbon_results], axis=1) #append all scens "raw" data

#FIX INDEX of dfs
sim_carbon_dfs.index = pd.RangeIndex(start=2000,stop=2101,step=1)
    
#return sim_carbon_results, sim_annual_carbon

PV_ICE
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_PERC
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_SHJ
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_TOPCon
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_IRENA
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
ex_Life
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
ex_High_eff
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
ex_Circular
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_EffLife
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_50PERC
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_RecycledPERC
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_Perovskite_life
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_Perovskite_Eff
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
In [211]:
#Do math on the carbon dfs, take in the output aggregate sim df
sim_annual_carbon = pd.DataFrame()
for scen in scenarios:
    mod_mfg_carbon_total = sim_carbon_dfs.filter(like=scen).filter(like='Global_mod_MFG') #annual mfging elec carbon
    mod_nonvMFG = ['Install','OandM','Repair','Demount','Store','Resell','ReMFG','Recycle'] #could remove from loop
    nonvMFG_search = '|'.join(mod_nonvMFG) #create nonRE search
    mod_carbon_sum_nonvmfg = sim_carbon_dfs.loc[:,sim_carbon_dfs.columns.str.contains(nonvMFG_search)].filter(like=scen).filter(like='_mod_') #annual non mfging carbon
    scen_annual_carbon_mod = pd.concat([mod_mfg_carbon_total,mod_carbon_sum_nonvmfg], axis=1)
    scen_annual_carbon_mod[scen+'_Annual_Emit_mod_elec_gCO2eq'] = scen_annual_carbon_mod.sum(axis=1)

    scenmatdcmaths = pd.DataFrame()
    for mat in MATERIALS:
        scen_mat_dc_temp = sim_carbon_dfs.filter(like=scen).filter(like=mat)
        #calculation for annual carbon emissions total (selecting to avoid double countings)
        mat_global_vmfg_elec = scen_mat_dc_temp.filter(like='Global_vmfg_elec') #select global mod mfging
        mat_vmfg_countries = scen_mat_dc_temp.filter(like='vmfg_elec') #select country specific mod mfging, includes global
        mat_emit_lifecycle = scen_mat_dc_temp.loc[:,~scen_mat_dc_temp.columns.isin(mat_vmfg_countries.columns)] #select everything not the two above
        
        scen_mat_annual_carbon = pd.concat([mat_global_vmfg_elec,mat_emit_lifecycle], axis=1) #group global mod, lifecycle
        scen_mat_annual_carbon[scen+'_Annual_Emit_'+mat+'_gCO2eq'] = scen_mat_annual_carbon.sum(axis=1) #sum annual emit
        
        scenmatdcmaths = pd.concat([scenmatdcmaths,scen_mat_annual_carbon], axis=1)
        #add by material
        #add by process, fuel, elec
        
        #mat_ce_recycle = mat_carbon_scen_results.filter(like='Recycle_e_p')
        #mat_ce_remfg = mat_carbon_scen_results.filter(like='ReMFG_clean')
        #mat_landfill = mat_carbon_scen_results.filter(like='landfill_total')
        #mat_scen_annual_carbon = pd.concat([mat_vmfg_total,mat_ce_recycle,mat_ce_remfg,mat_landfill], axis=1)
    scen_modmat_annual_carbon = pd.concat([scen_annual_carbon_mod,scenmatdcmaths], axis=1)
    scen_modmat_annual_carbon[scen+'_Annual_Emit_total_modmats_gCO2eq'] = scen_modmat_annual_carbon.filter(like='Annual_Emit').sum(axis=1)
    
    sim_annual_carbon = pd.concat([sim_annual_carbon, scen_modmat_annual_carbon], axis=1)


    #FIX INDEX of dfs
sim_annual_carbon.index = pd.RangeIndex(start=2000,stop=2101,step=1)
In [212]:
#create cumulative
sim_cumu_carbon = sim_annual_carbon.cumsum()
maxy = round(sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_total_modmats_gCO2eq').max()/1e12,-3) #for graphing
sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_total_modmats_gCO2eq')
Out[212]:
PV_ICE_Annual_Emit_total_modmats_gCO2eq               3.202837e+16
r_PERC_Annual_Emit_total_modmats_gCO2eq               2.412450e+16
r_SHJ_Annual_Emit_total_modmats_gCO2eq                2.296610e+16
r_TOPCon_Annual_Emit_total_modmats_gCO2eq             2.345077e+16
r_IRENA_Annual_Emit_total_modmats_gCO2eq              3.572808e+16
ex_Life_Annual_Emit_total_modmats_gCO2eq              2.552620e+16
ex_High_eff_Annual_Emit_total_modmats_gCO2eq          3.279803e+16
ex_Circular_Annual_Emit_total_modmats_gCO2eq          2.995347e+16
h_EffLife_Annual_Emit_total_modmats_gCO2eq            2.485862e+16
h_50PERC_Annual_Emit_total_modmats_gCO2eq             2.545395e+16
h_RecycledPERC_Annual_Emit_total_modmats_gCO2eq       2.659007e+16
h_Perovskite_life_Annual_Emit_total_modmats_gCO2eq    2.941659e+16
h_Perovskite_Eff_Annual_Emit_total_modmats_gCO2eq     2.589143e+16
Name: 2100, dtype: float64
In [213]:
#save the carbondataframe as csvs for now
sim_annual_carbon.to_csv(os.path.join(testfolder,'13scen_carbon_annual.csv'))
sim_cumu_carbon.to_csv(os.path.join(testfolder,'13scen_carbon_cumu.csv'))

Carbon Emissions Cumulative Scenario compare¶

In [214]:
sim_cumu_carbon_mmt = sim_cumu_carbon.filter(like='Annual_Emit_total_modmats_gCO2eq')/1e12
sim_cumu_carbon_mmt
Out[214]:
PV_ICE_Annual_Emit_total_modmats_gCO2eq r_PERC_Annual_Emit_total_modmats_gCO2eq r_SHJ_Annual_Emit_total_modmats_gCO2eq r_TOPCon_Annual_Emit_total_modmats_gCO2eq r_IRENA_Annual_Emit_total_modmats_gCO2eq ex_Life_Annual_Emit_total_modmats_gCO2eq ex_High_eff_Annual_Emit_total_modmats_gCO2eq ex_Circular_Annual_Emit_total_modmats_gCO2eq h_EffLife_Annual_Emit_total_modmats_gCO2eq h_50PERC_Annual_Emit_total_modmats_gCO2eq h_RecycledPERC_Annual_Emit_total_modmats_gCO2eq h_Perovskite_life_Annual_Emit_total_modmats_gCO2eq h_Perovskite_Eff_Annual_Emit_total_modmats_gCO2eq
2000 1.481254 1.350279 1.315947 1.332954 1.481254 1.413552 1.312583 1.481254 1.312583 1.413552 1.507282 1.481254 1.481254
2001 1.969319 1.837443 1.802876 1.819999 1.969320 1.901152 1.799488 1.969319 1.799488 1.901152 2.004598 1.969319 1.969319
2002 2.491843 2.325783 2.282255 2.303817 2.491843 2.406005 2.277990 2.491843 2.277990 2.406005 2.538466 2.491843 2.491843
2003 3.202322 2.989782 2.934070 2.961668 3.202320 3.092458 2.928612 3.202322 2.928612 3.092458 3.266599 3.202322 3.202322
2004 4.482671 4.186370 4.108702 4.147176 4.482653 4.329510 4.101092 4.482671 4.101092 4.329510 4.582126 4.482671 4.482671
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2096 30662.367272 23513.978870 22385.362978 22857.650861 34107.986846 23322.881575 31034.209492 29049.359358 24247.974208 24055.348931 25440.286262 28709.250828 25066.026895
2097 30961.510838 23723.247541 22584.421334 23060.953573 34524.693602 23887.254357 31490.226166 29276.596138 24392.804175 24420.665208 25725.807840 28878.546374 25271.414037
2098 31286.329197 23855.272003 22710.007105 23189.216888 34933.556233 24446.782542 31943.935761 29502.596786 24541.784087 24776.655983 26010.758933 29048.968015 25477.504456
2099 31639.429418 23988.835905 22837.057164 23318.975716 35333.130310 24995.122784 32376.409650 29728.288042 24696.408276 25121.593559 26298.437877 29220.747209 25684.171426
2100 32028.372868 24124.500003 22966.104890 23450.774808 35728.080361 25526.198587 32798.033201 29953.474587 24858.619278 25453.949771 26590.073117 29416.590984 25891.434797

101 rows × 13 columns

In [215]:
fig_cumu_carbon, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(15,5), sharey=True, sharex=True, 
                                      gridspec_kw={'wspace':0})

#BAU
ax1.set_prop_cycle(color=colorpalette[0:5])
ax1.plot(sim_cumu_carbon_mmt.iloc[:,0:5], label=scennames_labels_flat[0:5]) # baselines
ax1.set_title('Business as Usual', fontsize=14)
ax1.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes $CO_{2eq}$]', fontsize=20)
ax1.set_xlim(2000,2100)
ax1.legend(bbox_to_anchor=(0.9,-0.05))
ax1.set_ylim(0,maxy+1000)
ax1.xaxis.set_minor_locator(MultipleLocator(10))
ax1.grid(axis='both', which='both', color='0.9', ls='--') 


#Extreme
ax2.set_prop_cycle(color=colorpalette[5:8])

ax2.plot(sim_cumu_carbon_mmt.iloc[:,5:8], label=scennames_labels_flat[5:8])
ax2.xaxis.set_minor_locator(MultipleLocator(10))
ax2.grid(axis='both', which='both', color='0.9', ls='--') 

#create glowlines for Extreme scens
n_lines = 10
diff_linewidth = 1.05
alpha_value = 0.05
for n in range(1, n_lines+1):    
    ax2.plot(sim_cumu_carbon_mmt.iloc[:,5],
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            color=colorpalette[5])

for n in range(1, n_lines+1):    
    ax2.plot(sim_cumu_carbon_mmt.iloc[:,6],
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            color=colorpalette[6])

for n in range(1, n_lines+1):    
    ax2.plot(sim_cumu_carbon_mmt.iloc[:,7],
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            color=colorpalette[7])

ax2.set_title('Extreme', fontsize=14)
ax2.legend(bbox_to_anchor=(0.9,-0.05))

#Ambitious
ax3.set_prop_cycle(color=colorpalette[8:])
ax3.plot(sim_cumu_carbon_mmt.iloc[:,8:], label=scennames_labels_flat[8:], ls='--')# 
ax3.set_title('Ambitious', fontsize=14)
ax3.legend(bbox_to_anchor=(1.05,-0.05)) #(x,y)
ax3.xaxis.set_minor_locator(MultipleLocator(10))
ax3.grid(axis='both', which='both', color='0.9', ls='--') 

#overall figure

fig_cumu_carbon.suptitle('Cumulative Carbon Emissions', fontsize=24, y=1)
plt.show()
#fig_cumu_carbon.savefig('energyresults-annualMatDemands-decade.png', dpi=300, bbox_inches='tight')

Literature Validation¶

In [216]:
#comparing to Ember open source data, uses a lifecycle PV emission factor from IPCC for electricity carbon
ember_PVCO2 = pd.read_csv(os.path.join(carbonfolder,'Ember-PVEmissionsWorld2000-2022.csv'), index_col='year')
#ember_PVCO2['emissions_mtco2'] #ANNUAL DATA
ember_PVCO2_cumu = ember_PVCO2.cumsum()
In [217]:
#compare to Fthenakis and Leccisi 2021 analysis
FL2021_gwp_scSi2020 = 1010 #kg CO2eq/kWp from Fthenakis and Leccisi 2021 "sc_Si 2020"
FL2021_gwp_scSi2015 = 2000 #"scSi 2015"
FL2021_gwp_mcSi2020 = 1087 #mcSi 2020
FL2021_gwp_mcSi2015 = 1435 #mcSi 2015

kw_installed_pvice = sim1.scenario['PV_ICE'].dataIn_m['new_Installed_Capacity_[MW]']*1000 # kW installed

FL2021_gwp_range = pd.DataFrame(index=ember_PVCO2.index)
FL2021_gwp_range['F&L_sc-Si_2020'] = kw_installed_pvice.loc[:22].values*FL2021_gwp_scSi2020
FL2021_gwp_range['F&L_sc-Si_2015'] = kw_installed_pvice.loc[:22].values*FL2021_gwp_scSi2015
FL2021_gwp_range['F&L_mc-Si_2020'] = kw_installed_pvice.loc[:22].values*FL2021_gwp_mcSi2020
FL2021_gwp_range['F&L_mc-Si_2015'] = kw_installed_pvice.loc[:22].values*FL2021_gwp_mcSi2015

FL2021_gwp_range_cumu_mmt = FL2021_gwp_range.cumsum()/1e9 #cumulative, and kg to million metric tonnes
In [218]:
#compare to Ultra Low Carbon Solar Alliance South Korea rating, as redproduced in Polverini 2023
Polverini2023_low = 550 #kg CO2eq/kWp "France"
Polverini2023_high = 762 #kg CO2eq/kWp "China"

kw_installed_pvice = sim1.scenario['PV_ICE'].dataIn_m['new_Installed_Capacity_[MW]']*1000 # kW installed

Polverini2023_gwp_range = pd.DataFrame(index=ember_PVCO2.index)
Polverini2023_gwp_range['Polverini2023_low'] = kw_installed_pvice.loc[:22].values*Polverini2023_low
Polverini2023_gwp_range['Polverini2023_high'] = kw_installed_pvice.loc[:22].values*Polverini2023_high

Polverini2023_gwp_range_cumu_mmt = Polverini2023_gwp_range.cumsum()/1e9 #cumulative, and kg to million metric tonnes
In [219]:
#compare to Liang and You 2023, using Figure 1 2020 values from a and e
LiangYou2023_scSi_low = 250 #kg CO2eq/m2 ""
LiangYou2023_scSi_high = 350 #kg CO2eq/m2 ""
LiangYou2023_mcSi_low = 210
LiangYou2023_mcSi_high = 300

m2_installed_pvice = sim1.scenario['PV_ICE'].dataOut_m['Area'] # area deployed in m2

LiangYou2023_gwp_range = pd.DataFrame(index=ember_PVCO2.index)
LiangYou2023_gwp_range['LiangYou2023_scSi_low'] = m2_installed_pvice.loc[:22].values*LiangYou2023_scSi_low
LiangYou2023_gwp_range['LiangYou2023_scSi_high'] = m2_installed_pvice.loc[:22].values*LiangYou2023_scSi_high
LiangYou2023_gwp_range['LiangYou2023_mcSi_low'] = m2_installed_pvice.loc[:22].values*LiangYou2023_mcSi_low
LiangYou2023_gwp_range['LiangYou2023_mcSi_high'] = m2_installed_pvice.loc[:22].values*LiangYou2023_mcSi_high

LiangYou2023_gwp_range_cumu_mmt = LiangYou2023_gwp_range.cumsum()/1e9 #cumulative, and kg to million metric tonnes
In [220]:
plt.plot(sim_cumu_carbon_mmt.loc[:2022,'PV_ICE_Annual_Emit_total_modmats_gCO2eq'], label='PV_ICE', color='black')

plt.plot(LiangYou2023_gwp_range_cumu_mmt['LiangYou2023_scSi_low'], label='LiangYou2023_scSi_low', color='violet', ls='-.')
plt.plot(LiangYou2023_gwp_range_cumu_mmt['LiangYou2023_scSi_high'], label='LiangYou2023_scSi_high', color='mediumorchid', ls='-.')
plt.plot(LiangYou2023_gwp_range_cumu_mmt['LiangYou2023_mcSi_low'], label='LiangYou2023_mcSi_low', color='darkviolet', ls=':')
plt.plot(LiangYou2023_gwp_range_cumu_mmt['LiangYou2023_mcSi_high'], label='LiangYou2023_mcSi_high', color='blueviolet', ls=':')

plt.plot(FL2021_gwp_range_cumu_mmt['F&L_sc-Si_2020'], label='FthenakisLeccisi2021_sc-Si_2020', color='lightcoral', ls='-.')
plt.plot(FL2021_gwp_range_cumu_mmt.loc[:2015,'F&L_sc-Si_2015'], label='FthenakisLeccisi2021_sc-Si_2015', color='indianred', ls='-.')
plt.plot(FL2021_gwp_range_cumu_mmt['F&L_mc-Si_2020'], label='FthenakisLeccisi2021_mc-Si_2020', color='firebrick', ls='dotted')
plt.plot(FL2021_gwp_range_cumu_mmt.loc[:2015,'F&L_mc-Si_2015'], label='FthenakisLeccisi2021_mc-Si_2015', color='maroon', ls='dotted')

plt.plot(Polverini2023_gwp_range_cumu_mmt['Polverini2023_low'], label='Polverini2023_low', color='deepskyblue', ls='--')
plt.plot(Polverini2023_gwp_range_cumu_mmt['Polverini2023_high'], label='Polverini2023_high', color='dodgerblue', ls='--')

plt.plot(ember_PVCO2_cumu.index, ember_PVCO2_cumu['emissions_mtco2'], label='Ember_electricity', color='green', ls='--')

plt.ylabel('Cumulative Carbon\n[$CO_{2eq}$ million metric tonnes]')
plt.title('Cumulative Carbon Emissions from PV')
plt.xlim(2000,2025)
plt.ylim(0,)
plt.legend(loc='upper left', fontsize=12)
Out[220]:
<matplotlib.legend.Legend at 0x26160d09250>

Flip it, compare on CO2eq/kWp¶

This is a simple way of doing it, will not work for much beyond 2022, because the annual emissions include end of life of other systems, not just the mfging of installed - its not necessarily fair. Might work out on balance, kinda

In [221]:
#lit factors into scatter points
litfactors = pd.DataFrame(index=ember_PVCO2_cumu.index)

litfactors.loc[2022,'Polverini2023_low'] = Polverini2023_low
litfactors.loc[2022,'Polverini2023_high'] = Polverini2023_high
litfactors.loc[2020,'FthenakisLeccisi2021_scSi'] = FL2021_gwp_scSi2020 
litfactors.loc[2015,'FthenakisLeccisi2021_scSi'] = FL2021_gwp_scSi2015 
litfactors.loc[2020,'FthenakisLeccisi2021_mcSi'] = FL2021_gwp_mcSi2020
litfactors.loc[2015,'FthenakisLeccisi2021_mcSi'] = FL2021_gwp_mcSi2015

litfactors.loc[2020,'LiangYou2023_scSi_m$^{2}$'] = LiangYou2023_scSi_low #kg CO2eq/m2 ""
litfactors.loc[2019,'LiangYou2023_scSi_m$^{2}$'] = LiangYou2023_scSi_high  #kg CO2eq/m2 ""
litfactors.loc[2020,'LiangYou2023_mcSi_m$^{2}$'] = LiangYou2023_mcSi_low 
litfactors.loc[2019,'LiangYou2023_mcSi_m$^{2}$'] = LiangYou2023_mcSi_high

litfactors.loc[2015,'Anctil2021_low'] = 1010
litfactors.loc[2015,'Anctil2021_high'] = 1775
litfactors.loc[2020,'Anctil2021_low'] = 500
litfactors.loc[2020,'Anctil2021_high'] = 750

litfactors.loc[2005,'Jungbluth2005_scSi_m$^{2}$'] = 170 #scSi CO2fossil, might be just module not full lifecycle

litfactors.loc[2020,'Wikoff2022_low_m$^{2}$'] = 125
litfactors.loc[2020,'Wikoff2022_high_m$^{2}$'] = 275


#litfactors
In [222]:
#calculate CO2eq/kWp by dividing annual CO2eq/deployed PV
installs = pd.DataFrame(kw_installed_pvice.loc[:22])
installs.index = ember_PVCO2_cumu.index

pvice_emit_annual = sim_annual_carbon.filter(like='PV_ICE').filter(like='Annual_Emit_total_modmats_gCO2eq').loc[:2022]
pvice_annual_kgco2pkwp = pvice_emit_annual['PV_ICE_Annual_Emit_total_modmats_gCO2eq'].div(installs['new_Installed_Capacity_[MW]'], axis=0)/1e3
#pvice_annual_kgco2pkwp
In [223]:
#calculate CO2eq/m2 for PV ICE
meters2installs = pd.DataFrame(m2_installed_pvice.loc[:22])
meters2installs.index = ember_PVCO2_cumu.index

pvice_annual_kgco2eqpm2 = pvice_emit_annual['PV_ICE_Annual_Emit_total_modmats_gCO2eq'].div(meters2installs['Area'], axis=0)/1e3
#pvice_annual_kgco2eqpm2
In [224]:
litfactors.columns
Out[224]:
Index(['Polverini2023_low', 'Polverini2023_high', 'FthenakisLeccisi2021_scSi',
       'FthenakisLeccisi2021_mcSi', 'LiangYou2023_scSi_m$^{2}$',
       'LiangYou2023_mcSi_m$^{2}$', 'Anctil2021_low', 'Anctil2021_high',
       'Jungbluth2005_scSi_m$^{2}$', 'Wikoff2022_low_m$^{2}$',
       'Wikoff2022_high_m$^{2}$'],
      dtype='object')
In [225]:
#graphing

plt.plot(pvice_annual_kgco2pkwp, label='PV_ICE kg CO$_{2}$eq/kW$_{p}$', color='black')

plt.scatter(litfactors.index, litfactors['Polverini2023_low'], label='Polverini2023_low', color='deepskyblue')
plt.scatter(litfactors.index, litfactors['Polverini2023_high'], label='Polverini2023_high', color='dodgerblue')

plt.scatter(litfactors.index, litfactors['FthenakisLeccisi2021_scSi'], label='FthenakisLeccisi2021_scSi', color='lightcoral')
plt.scatter(litfactors.index, litfactors['FthenakisLeccisi2021_mcSi'], label='FthenakisLeccisi2021_mcSi', color='firebrick')

plt.scatter(litfactors.index, litfactors['Anctil2021_low'], label='Anctil2021_low', color='green')
plt.scatter(litfactors.index, litfactors['Anctil2021_high'], label='Anctil2021_high', color='limegreen')


#plt.plot(pvice_annual_kgco2eqpm2, label='PV_ICE kg CO$_{2}$eq/m$^{2}$', color='darkgray', marker='^')

#plt.scatter(litfactors.index, litfactors['LiangYou2023_scSi_m$^{2}$'], label='LiangYou2023_scSi_m$^{2}$', color='violet', marker='^')
#plt.scatter(litfactors.index, litfactors['LiangYou2023_mcSi_m$^{2}$'], label='LiangYou2023_mcSi_m$^{2}$', color='darkviolet', marker='^')

#plt.scatter(litfactors.index, litfactors['Jungbluth2005_scSi_m$^{2}$'], label='Jungbluth2005_scSi_m$^{2}$', color='orange', marker='^')
plt.ylim(0,2500)
plt.ylabel('kg CO$_{2}$eq/kW$_{p}$ OR kg CO$_{2}$eq/m$^{2}$')
plt.title('Literature Comparison:\nkg CO$_{2}$eq/kW$_{p}$')
plt.legend(bbox_to_anchor=(1.6,1))
Out[225]:
<matplotlib.legend.Legend at 0x2615e620250>
In [226]:
plt.plot(pvice_annual_kgco2eqpm2, label='PV_ICE kg CO$_{2}$eq/m$^{2}$', color='darkgray', marker='^')

plt.scatter(litfactors.index, litfactors['LiangYou2023_scSi_m$^{2}$'], label='LiangYou2023_scSi_m$^{2}$', color='violet', marker='^')
plt.scatter(litfactors.index, litfactors['LiangYou2023_mcSi_m$^{2}$'], label='LiangYou2023_mcSi_m$^{2}$', color='darkviolet', marker='^')

plt.scatter(litfactors.index, litfactors['Jungbluth2005_scSi_m$^{2}$'], label='Jungbluth2005_scSi_m$^{2}$', color='orange', marker='^')

plt.scatter(litfactors.index, litfactors['Wikoff2022_low_m$^{2}$'], label='Wikoff2022_low_m$^{2}$', color='fuchsia', marker='^')
plt.scatter(litfactors.index, litfactors['Wikoff2022_high_m$^{2}$'], label='Wikoff2022_high_m$^{2}$', color='deeppink', marker='^')

plt.ylim(0,375)
plt.ylabel('kg CO$_{2}$eq/m$^{2}$')
plt.title('Literature Comparison:\nkg CO$_{2}$eq/m$^{2}$')
plt.legend(bbox_to_anchor=(1.6,1))
Out[226]:
<matplotlib.legend.Legend at 0x2615719a090>

Contextualize versus Global Carbon Emissions and Budget¶

In [227]:
annualco2emitglobal_raw = pd.read_csv(os.path.join(carbonfolder,'WorldInData-annual-co2-emissions-per-country.csv'))
annualco2emitglobal_subset = annualco2emitglobal_raw.loc[(annualco2emitglobal_raw['Entity']=='World')&(annualco2emitglobal_raw['Year']>=2000), 'Year':]
annualco2emitglobal_subset.index=annualco2emitglobal_subset['Year']

#create % PV emit vs world
pvice_annualPVemit_bmt = sim_annual_carbon.loc[:2022,'PV_ICE_Annual_Emit_total_modmats_gCO2eq']/1e15 #bmt
world_annual_bmt = annualco2emitglobal_subset.iloc[:,1]/1e9 #bmt

percentPVemitvsWorld = pvice_annualPVemit_bmt/world_annual_bmt*100
In [228]:
#cumulative compare
sim_cumu_carbon_bmt = sim_cumu_carbon_mmt.loc[:,'PV_ICE_Annual_Emit_total_modmats_gCO2eq']/1e3
sim_cumu_carbon_bmt
Out[228]:
2000     0.001481
2001     0.001969
2002     0.002492
2003     0.003202
2004     0.004483
          ...    
2096    30.662367
2097    30.961511
2098    31.286329
2099    31.639429
2100    32.028373
Name: PV_ICE_Annual_Emit_total_modmats_gCO2eq, Length: 101, dtype: float64
In [229]:
fig_worldcompare, ax1 = plt.subplots()

ax1.plot(annualco2emitglobal_subset.iloc[:,1]/1e9, label = 'World Emissions', color='red') #bmt
ax1.plot(sim_annual_carbon.loc[:2022,'PV_ICE_Annual_Emit_total_modmats_gCO2eq']/1e15,
         label='PV_ICE PV Emissions', color='black')
ax1.set_ylim(0,40)
ax1.set_ylabel('Emissions\n[billion metric tonnes $CO_{2eq}$]')
ax1.set_xlim(2000,2023)

ax2 = ax1.twinx()
ax2.plot(percentPVemitvsWorld,ls=':', color='blue')
ax2.set_ylabel('PV Percent of World Emissions [%]', color='blue')
ax2.set_ylim(0,0.5)
#ax2.set_xlim(2000,2022)

plt.title('Compare PV Emissions to Global Emissions')
ax1.legend(loc='center left')
plt.show()
In [230]:
fig_worldcompare, ax1 = plt.subplots()

ax1.plot(annualco2emitglobal_subset.iloc[:,1]/1e9, label = 'World Annual Emissions', color='red') #bmt
ax1.plot(sim_cumu_carbon_bmt, label='PV_ICE Cumulative Emissions', color='black')
ax1.set_ylim(0,40)
ax1.set_ylabel('Emissions\n[billion metric tonnes $CO_{2eq}$]')
ax1.set_xlim(2000,2100)

#ax2 = ax1.twinx()
#ax2.plot(percentPVemitvsWorld,ls=':', color='blue')
#ax2.set_ylabel('PV Percent of World Emissions [%]', color='blue')
#ax2.set_ylim(0,0.5)
#ax2.set_xlim(2000,2022)

plt.title('Compare:\nPV Cumulative Emissions\nvs Global Annual Emissions')
ax1.legend(loc='upper right')
plt.show()
In [231]:
annualEmitGlobal_SSPs = pd.read_csv(os.path.join(supportMatfolder,'CarbonIntensities','input-CarbonEmitProjections-SSP15.csv'),
                                   skiprows=1, index_col=0)
In [232]:
annualEmitGlobal_SSPs.iloc[:,0:2] = annualEmitGlobal_SSPs.iloc[:,0:2].interpolate()
#as percent of global emit scenarios SSPs
pvice_annualPVemit_full_bmt = sim_annual_carbon.loc[:,'PV_ICE_Annual_Emit_total_modmats_gCO2eq']/1e15
percentPVemitvsWorld_SSP1 = pvice_annualPVemit_full_bmt/annualEmitGlobal_SSPs['SSP1_IMAGE_[GtCO2eq/year]']*100
percentPVemitvsWorld_SSP5 = pvice_annualPVemit_full_bmt/annualEmitGlobal_SSPs['SSP5_REMIND_[GtCO2eq/year]']*100
In [233]:
fig_worldcompare, ax1 = plt.subplots()

ax1.plot(annualco2emitglobal_subset.iloc[:,1]/1e9, label = 'Historical World Emissions', color='black') #bmt

ax1.plot(annualEmitGlobal_SSPs['SSP1_IMAGE_[GtCO2eq/year]'], label='SSP1', color='blue')
ax1.plot(annualEmitGlobal_SSPs['SSP5_REMIND_[GtCO2eq/year]'], label='SSP5', color='red')

ax1.plot(sim_annual_carbon.loc[:2022,'PV_ICE_Annual_Emit_total_modmats_gCO2eq']/1e15,
         label='PV_ICE PV Emissions', color='aqua')
ax1.set_ylim(0,140)
ax1.set_ylabel('Emissions\n[billion metric tonnes $CO_{2eq}$]')
ax1.set_xlim(2000,2100)

ax2 = ax1.twinx()
ax2.plot(percentPVemitvsWorld, label='Percent Historic Emissions', ls=':', color='black')
ax2.plot(percentPVemitvsWorld_SSP1, label='Percent SSP1 Low scenario Emissions', ls=':', color='orange')
ax2.plot(percentPVemitvsWorld_SSP5, label='Percent SSP5 High scenario Emissions', ls=':', color='skyblue')

percentPVemitvsWorld_SSP1
ax2.set_ylabel('PV Percent of World Emissions [%]')
ax2.set_ylim(0,100)
#ax2.set_xlim(2000,2022)

plt.title('Compare PV Emissions to Global Emissions')
plt.legend(loc='upper left')
plt.show()
In [ ]:

Cabon Emissions by material or module¶

In [234]:
for scen in scenarios:

    scen_annual_carbon = sim_annual_carbon.filter(like='Annual_Emit').filter(like=scen)/1e12 #million tonnes
    
    plt.plot([],[],color=colormats[0], label=MATERIALS[0])
    plt.plot([],[],color=colormats[1], label=MATERIALS[1])
    plt.plot([],[],color=colormats[2], label=MATERIALS[2])
    plt.plot([],[],color=colormats[3], label=MATERIALS[3])
    plt.plot([],[],color=colormats[4], label=MATERIALS[4])
    plt.plot([],[],color=colormats[5], label=MATERIALS[5])
    plt.plot([],[],color=colormats[6], label=MATERIALS[6])
    plt.plot([],[],color=colormats[7], label='module')


    plt.stackplot(scen_annual_carbon.index,
                  scen_annual_carbon[scen+'_Annual_Emit_glass_gCO2eq'], 
                  scen_annual_carbon[scen+'_Annual_Emit_silicon_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_silver_gCO2eq'], 
                  scen_annual_carbon[scen+'_Annual_Emit_aluminium_frames_gCO2eq'], 
                  scen_annual_carbon[scen+'_Annual_Emit_copper_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_encapsulant_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_backsheet_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_mod_elec_gCO2eq'],
                  colors = colormats)
    plt.title(scen+':\nGHG Emissions Annually by Module and Material Lifecycle')
    plt.ylabel('GHG Emissions Annually from Lifecycle Mats and Mods\n[million metric tonnes $CO_{2eq}$]')
    plt.xlim(2000,2100)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.45,1))

#plt.legend()
    plt.show()
In [ ]:
In [235]:
maxy
Out[235]:
36000.0
In [236]:
#colormats = ['#00bfbf','#ff7f0e','#1f77be','#2ca02c','#d62728','#9467BD','#8C564B','black'] #colors for material plots
for scen in scenarios:

    scen_cumu_carbon = sim_cumu_carbon.filter(like='Annual_Emit').filter(like=scen)/1e12 #million tonnes
    
    plt.plot([],[],color=colormats[0], label=MATERIALS[0])
    plt.plot([],[],color=colormats[1], label=MATERIALS[1])
    plt.plot([],[],color=colormats[2], label=MATERIALS[2])
    plt.plot([],[],color=colormats[3], label=MATERIALS[3])
    plt.plot([],[],color=colormats[4], label=MATERIALS[4])
    plt.plot([],[],color=colormats[5], label=MATERIALS[5])
    plt.plot([],[],color=colormats[6], label=MATERIALS[6])
    plt.plot([],[],color=colormats[7], label='module')


    plt.stackplot(scen_cumu_carbon.index,
                  scen_cumu_carbon[scen+'_Annual_Emit_glass_gCO2eq'], 
                  scen_cumu_carbon[scen+'_Annual_Emit_silicon_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_silver_gCO2eq'], 
                  scen_cumu_carbon[scen+'_Annual_Emit_aluminium_frames_gCO2eq'], 
                  scen_cumu_carbon[scen+'_Annual_Emit_copper_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_encapsulant_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_backsheet_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_mod_elec_gCO2eq'],
                  colors = colormats)
    plt.title(scen+':\nGHG Emissions Annually by Module and Material Lifecycle')
    plt.ylabel('GHG Emissions Annually from Lifecycle Mats and Mods\n[million metric tonnes $CO_{2eq}$]')
    plt.xlim(2000,2100)
    plt.ylim(0,maxy+1000)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.45,1))
    plt.grid(axis='both', which='both', color='0.9', ls='--')
#plt.legend()
    plt.show()
In [237]:
sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_mod_elec')
Out[237]:
PV_ICE_Annual_Emit_mod_elec_gCO2eq               1.023026e+14
r_PERC_Annual_Emit_mod_elec_gCO2eq               9.387779e+13
r_SHJ_Annual_Emit_mod_elec_gCO2eq                8.937543e+13
r_TOPCon_Annual_Emit_mod_elec_gCO2eq             9.124345e+13
r_IRENA_Annual_Emit_mod_elec_gCO2eq              1.200841e+14
ex_Life_Annual_Emit_mod_elec_gCO2eq              9.572272e+13
ex_High_eff_Annual_Emit_mod_elec_gCO2eq          7.641418e+13
ex_Circular_Annual_Emit_mod_elec_gCO2eq          1.463938e+14
h_EffLife_Annual_Emit_mod_elec_gCO2eq            8.099209e+13
h_50PERC_Annual_Emit_mod_elec_gCO2eq             9.631957e+13
h_RecycledPERC_Annual_Emit_mod_elec_gCO2eq       3.570136e+14
h_Perovskite_life_Annual_Emit_mod_elec_gCO2eq    1.381900e+14
h_Perovskite_Eff_Annual_Emit_mod_elec_gCO2eq     1.251060e+14
Name: 2100, dtype: float64
In [238]:
#create a df from which to do a bar chart of 2100 emissions by mat/mod
mats_emit_2100 = pd.DataFrame() #index=scennames_labels_flat
for mat in MATERIALS:
    mat_emit_2100 = pd.Series(sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_'+mat).values)
    mats_emit_2100 = pd.concat([mats_emit_2100, mat_emit_2100], axis=1)

mats_emit_2100
mats_emit_2100.columns = MATERIALS
modmats_emit_2100 = pd.concat([mats_emit_2100,pd.Series(sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_mod_elec').values)], axis=1)
modmats_emit_2100.index = scennames_labels_flat
modmats_emit_2100.rename(columns={0:'module'}, inplace=True)
modmats_emit_2100_megatonne = modmats_emit_2100/1e12
modmats_emit_2100_megatonne
Out[238]:
glass silicon silver aluminium_frames copper encapsulant backsheet module
PV_ICE 10284.668445 8352.915465 41.716861 7929.612399 8.876447 3061.671748 2246.608915 102.302588
PERC 9211.126614 6836.123265 27.187171 5089.969603 8.208785 2836.277498 21.729280 93.877786
SHJ 8757.747634 6503.772557 48.164047 4840.895039 7.806898 2697.012716 21.330570 89.375430
TOPCon 8944.464134 6641.044831 46.567178 4943.554739 7.972690 2754.399712 21.528079 91.243446
Low Quality 11758.651199 8094.473353 43.233520 7311.873346 13.480864 4840.730291 3545.553651 120.084137
Long-Lived 9811.997185 6785.834763 32.530383 6394.853886 7.311261 2375.485270 22.463119 95.722720
High Eff 13115.880866 8069.438369 40.400063 8312.118492 9.008162 3153.481497 21.291570 76.414183
Circular 9064.432035 5877.909710 96.765829 6379.837582 22.426980 8342.458216 23.250434 146.393801
High Eff + Long-life 9684.311380 6441.389400 33.105208 6251.545324 7.019577 2338.964802 21.291502 80.992086
Long-Life + Recycling 9904.249725 6904.964917 35.476460 5999.931789 7.373947 2483.169997 22.463363 96.319573
Recycled-Si + Long-life 9142.634864 4628.650653 36.178543 5070.660204 11.986646 4237.396671 3105.551983 357.013553
Circular + Long-life 8968.881721 8531.634829 69.904197 6054.806967 15.615571 5614.307309 23.250434 138.189957
Circular + High Eff 7002.525037 7814.201104 72.803760 4772.243304 16.553479 6064.751648 23.250434 125.106030
In [239]:
fig_cumuemit_modmat, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['glass'], color=colormats[0])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['silicon'],
        bottom=modmats_emit_2100_megatonne[0:5]['glass'], color=colormats[1])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['silver'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:2].sum(axis=1), color=colormats[2])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['aluminium_frames'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:3].sum(axis=1), color=colormats[3])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['copper'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:4].sum(axis=1), color=colormats[4])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['encapsulant'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:5].sum(axis=1), color=colormats[5])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['backsheet'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:6].sum(axis=1), color=colormats[6])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['module'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:7].sum(axis=1), color='black')

ax0.set_ylim(0,maxy+1000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes $CO_{2eq}$]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.6', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['glass'], color=colormats[0])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['silicon'],
        bottom=modmats_emit_2100_megatonne[5:8]['glass'], color=colormats[1])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['silver'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:2].sum(axis=1), color=colormats[2])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['aluminium_frames'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:3].sum(axis=1), color=colormats[3])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['copper'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:4].sum(axis=1), color=colormats[4])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['encapsulant'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:5].sum(axis=1), color=colormats[5])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['backsheet'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:6].sum(axis=1), color=colormats[6])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['module'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:7].sum(axis=1), color='black')

ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.6', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['glass'], color=colormats[0])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['silicon'],
        bottom=modmats_emit_2100_megatonne[8:]['glass'], color=colormats[1])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['silver'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:2].sum(axis=1), color=colormats[2])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['aluminium_frames'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:3].sum(axis=1), color=colormats[3])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['copper'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:4].sum(axis=1), color=colormats[4])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['encapsulant'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:5].sum(axis=1), color=colormats[5])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['backsheet'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:6].sum(axis=1), color=colormats[6])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['module'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:7].sum(axis=1), color='black')


ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.6', ls='--') 
ax3.set_axisbelow(True)

#overall fig

fig_cumuemit_modmat.suptitle('Cumulative Emisisons in 2100 by material', fontsize=24)
plt.show()

#fig_cumuemit_modmat.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_7420\3630820325.py:23: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_7420\3630820325.py:45: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_7420\3630820325.py:68: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
In [240]:
cumu_emit_sum = modmats_emit_2100_megatonne.sum(axis=1)
fraction_modmats_cumu_emit = modmats_emit_2100_megatonne.div(cumu_emit_sum, axis=0)*100
fraction_modmats_cumu_emit
Out[240]:
glass silicon silver aluminium_frames copper encapsulant backsheet module
PV_ICE 32.111117 26.079737 0.130250 24.758087 0.027714 9.559248 7.014433 0.319412
PERC 38.181627 28.336850 0.112695 21.098757 0.034027 11.756834 0.090071 0.389139
SHJ 38.133361 28.319006 0.209718 21.078433 0.033993 11.743449 0.092878 0.389162
TOPCon 38.141444 28.319085 0.198574 21.080560 0.033998 11.745453 0.091801 0.389085
Low Quality 32.911511 22.655775 0.121007 20.465341 0.037732 13.548812 9.923717 0.336106
Long-Lived 38.438928 26.583805 0.127439 25.052120 0.028642 9.306067 0.088000 0.374998
High Eff 39.989840 24.603422 0.123178 25.343344 0.027466 9.614849 0.064917 0.232984
Circular 30.261705 19.623465 0.323054 21.299157 0.074873 27.851387 0.077622 0.488737
High Eff + Long-life 38.957559 25.912096 0.133174 25.148401 0.028238 9.409070 0.085650 0.325811
Long-Life + Recycling 38.910463 27.127283 0.139375 23.571712 0.028970 9.755539 0.088251 0.378407
Recycled-Si + Long-life 34.383639 17.407439 0.136060 19.069749 0.045079 15.936010 11.679366 1.342657
Circular + Long-life 30.489195 29.002799 0.237635 20.582966 0.053084 19.085513 0.079039 0.469769
Circular + High Eff 27.045720 30.180641 0.281189 18.431745 0.063934 23.423776 0.089800 0.483195
In [241]:
fig_cumuemit_modmat, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['glass'], color=colormats[0])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['silicon'],
        bottom=fraction_modmats_cumu_emit[0:5]['glass'], color=colormats[1])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['silver'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:2].sum(axis=1), color=colormats[2])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['aluminium_frames'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:3].sum(axis=1), color=colormats[3])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['copper'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:4].sum(axis=1), color=colormats[4])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['encapsulant'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:5].sum(axis=1), color=colormats[5])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['backsheet'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:6].sum(axis=1), color=colormats[6])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['module'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:7].sum(axis=1), color='black')

ax0.set_ylim(0,100)
ax0.set_ylabel('Percent Emissions by Material\n[%]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.6', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['glass'], color=colormats[0])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['silicon'],
        bottom=fraction_modmats_cumu_emit[5:8]['glass'], color=colormats[1])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['silver'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:2].sum(axis=1), color=colormats[2])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['aluminium_frames'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:3].sum(axis=1), color=colormats[3])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['copper'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:4].sum(axis=1), color=colormats[4])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['encapsulant'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:5].sum(axis=1), color=colormats[5])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['backsheet'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:6].sum(axis=1), color=colormats[6])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['module'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:7].sum(axis=1), color='black')

ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.6', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['glass'], color=colormats[0])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['silicon'],
        bottom=fraction_modmats_cumu_emit[8:]['glass'], color=colormats[1])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['silver'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:2].sum(axis=1), color=colormats[2])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['aluminium_frames'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:3].sum(axis=1), color=colormats[3])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['copper'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:4].sum(axis=1), color=colormats[4])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['encapsulant'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:5].sum(axis=1), color=colormats[5])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['backsheet'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:6].sum(axis=1), color=colormats[6])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['module'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:7].sum(axis=1), color='black')


ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.6', ls='--') 
ax3.set_axisbelow(True)

#overall fig

fig_cumuemit_modmat.suptitle('Fraction of Emissions attribute to Module/Material', fontsize=24)
plt.show()

#fig_cumuemit_modmat.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_7420\2389923115.py:23: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_7420\2389923115.py:45: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_7420\2389923115.py:68: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
In [ ]:

Cumulative Carbon in 2050 and 2100¶

In [242]:
#mins in 2050 and 2100
cumu_carbon_2050 = sim_cumu_carbon.loc[2050].filter(like='Annual_Emit_total_modmats')/1e12
cumu_carbon_2100 = sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_total_modmats')/1e12
cumu_carbon_rankings_crittime = pd.concat([cumu_carbon_2050,cumu_carbon_2100], axis=1)
cumu_carbon_rankings_crittime.index = scennames_labels_flat
cumu_carbon_rankings_crittime_bmt = cumu_carbon_rankings_crittime/1000
round(cumu_carbon_rankings_crittime_bmt,1)
Out[242]:
2050 2100
PV_ICE 15.3 32.0
PERC 13.9 24.1
SHJ 13.3 23.0
TOPCon 13.5 23.5
Low Quality 16.8 35.7
Long-Lived 14.5 25.5
High Eff 11.9 32.8
Circular 19.3 30.0
High Eff + Long-life 12.3 24.9
Long-Life + Recycling 14.6 25.5
Recycled-Si + Long-life 15.8 26.6
Circular + Long-life 20.1 29.4
Circular + High Eff 16.9 25.9
In [243]:
cumu_carbon_rankings_crittime_plot = cumu_carbon_rankings_crittime.copy()
cumu_carbon_rankings_crittime_plot['diff'] = cumu_carbon_rankings_crittime[2100]-cumu_carbon_rankings_crittime[2050]
In [244]:
fig_cumulativeemit, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(cumu_carbon_rankings_crittime_plot.index[0:5], cumu_carbon_rankings_crittime_plot[2050].iloc[0:5],
        tick_label=scennames_labels[0:5], color=colorpalette[0:5], alpha = 0.7, edgecolor='white')
ax0.bar(cumu_carbon_rankings_crittime_plot.index[0:5], cumu_carbon_rankings_crittime_plot['diff'].iloc[0:5],
        bottom=cumu_carbon_rankings_crittime_plot[2050].iloc[0:5],
        tick_label=scennames_labels[0:5], color=colorpalette[0:5])
ax0.set_ylim(0,maxy+1000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes $CO_{2eq}$]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.6', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(cumu_carbon_rankings_crittime_plot.index[5:8], cumu_carbon_rankings_crittime_plot[2050].iloc[5:8],
        tick_label=scennames_labels[5:8], color=colorpalette[5:8], alpha = 0.7, edgecolor='white')
ax2.bar(cumu_carbon_rankings_crittime_plot.index[5:8], cumu_carbon_rankings_crittime_plot['diff'].iloc[5:8],
        bottom=cumu_carbon_rankings_crittime_plot[2050].iloc[5:8],
        tick_label=scennames_labels[5:8], color=colorpalette[5:8])
ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.6', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(cumu_carbon_rankings_crittime_plot.index[8:], cumu_carbon_rankings_crittime_plot[2050].iloc[8:],
        tick_label=scennames_labels[8:], color=colorpalette[8:], hatch='x', edgecolor='white', alpha=0.7)
ax3.bar(cumu_carbon_rankings_crittime_plot.index[8:], cumu_carbon_rankings_crittime_plot['diff'].iloc[8:],
        bottom=cumu_carbon_rankings_crittime_plot[2050].iloc[8:],
        tick_label=scennames_labels[8:], color=colorpalette[8:], hatch='x', edgecolor='white')
ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.6', ls='--') 
ax3.set_axisbelow(True)

#overall fig

fig_cumulativeemit.suptitle('Cumulative Emissions in 2050, 2100', fontsize=24)
plt.show()

fig_cumulativeemit.savefig(os.path.join(testfolder,'carbonresults_CUMU20502100.png'), dpi=300, bbox_inches='tight')

¶

Emissions by electricity vs fuels vs process¶

Process emission summing¶

This only happens on the material files

In [245]:
process_emissions = pd.DataFrame()
for scen in scenarios:
    scen_p = sim_carbon_dfs.filter(like=scen).filter(like='_p_')
    scen_p_sum = scen_p.sum(axis=1)
    process_emissions = pd.concat([process_emissions,scen_p_sum], axis=1)

process_emissions.columns = scennames_labels_flat
process_emissions.index = pd.RangeIndex(start=2000,stop=2101,step=1)
process_emissions_cumu = process_emissions.cumsum()
In [246]:
#process_emissions_cumu

Fuel Emissions¶

This is capturing steam and heating fuel, also only on material level

In [247]:
fuel_emissions = pd.DataFrame()
for scen in scenarios:
    scen_f = sim_carbon_dfs.filter(like=scen).filter(like='_fuel_')
    scen_f_sum = scen_f.sum(axis=1)
    fuel_emissions = pd.concat([fuel_emissions,scen_f_sum], axis=1)
    
fuel_emissions.columns = scennames_labels_flat
fuel_emissions.index = pd.RangeIndex(start=2000,stop=2101,step=1)
fuel_emissions_cumu = fuel_emissions.cumsum()
In [248]:
#fuel_emissions_cumu

Electricity Emissions¶

both module and material level elec.

In [249]:
elec_emissions = pd.DataFrame()
for scen in scenarios:
    scen_mod_elec = sim_carbon_dfs.filter(like=scen).filter(like='Global_mod_MFG') #module elec lifecycle emits
    
    #material elec emits
    mat_eleckey = ['Global_vmfg_elec','landfill_elec','ReMFG_clean_elec','Recycled_LQ_elec','Recycled_HQ_elec']
    mat_elecs_search = '|'.join(mat_eleckey)
    scen_mat_elecs = sim_carbon_dfs.loc[:,sim_carbon_dfs.columns.str.contains(mat_elecs_search)].filter(like=scen)
    scen_mat_elecs_sum = scen_mat_elecs.sum(axis=1)
    
    #sum them together by scen
    scen_elec_modmat_annual_sum = scen_mat_elecs_sum+scen_mod_elec.iloc[:,0]
    elec_emissions = pd.concat([elec_emissions,scen_elec_modmat_annual_sum], axis=1)
    
elec_emissions.columns=scennames_labels_flat
elec_emissions.index = pd.RangeIndex(start=2000,stop=2101,step=1)
elec_emissions_cumu = elec_emissions.cumsum()
In [250]:
#graphing by emission source
efp_emit_total = elec_emissions+fuel_emissions+process_emissions
efp_emit_total_cumu = elec_emissions_cumu+fuel_emissions_cumu+process_emissions_cumu
In [251]:
#graphing by emission source, annual
#efp_emit_total = elec_emissions+fuel_emissions+process_emissions

for scen in scennames_labels_flat:
    
    plt.plot([],[],color='black', label='process')
    plt.plot([],[],color='darkred', label='fuel')
    plt.plot([],[],color='blue', label='electricity')

    plt.stackplot(elec_emissions.index,
                  process_emissions[scen]/1e12, 
                  fuel_emissions[scen]/1e12,
                  elec_emissions[scen]/1e12, 
                  colors = ['black','darkred','blue'])
    plt.title(scen+':\nGHG Emissions Annually by Source')
    plt.ylabel('GHG Emissions Annually from Lifecycle Source\n[million metric tonnes $CO_{2eq}$]')
    plt.xlim(2000,2100)
    plt.ylim(0,)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.4,1))
    #plt.grid(axis='both', which='both', color='0.9', ls='--', zorder=0)
    #plt.set_axisbelow(True)
#plt.legend()
    plt.show()
In [252]:
#graphing by emission source, cumulative

for scen in scennames_labels_flat:
    
    plt.plot([],[],color='black', label='process')
    plt.plot([],[],color='darkred', label='fuel')
    plt.plot([],[],color='blue', label='electricity')

    plt.stackplot(elec_emissions_cumu.index,
                  process_emissions_cumu[scen]/1e12, 
                  fuel_emissions_cumu[scen]/1e12,
                  elec_emissions_cumu[scen]/1e12, 
                  colors = ['black','darkred','blue'])
    plt.title(scen+':\nGHG Emissions Cumulative by Source')
    plt.ylabel('GHG Emissions Cumulatively from Lifecycle Source\n[million metric tonnes $CO_{2eq}$]')
    plt.xlim(2000,2100)
    plt.ylim(0,maxy+1000)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.3,1))
    plt.grid(axis='both', which='both', color='0.9', ls='--')
#plt.legend()
    plt.show()
In [253]:
#bar chart 2050 and 2100 by scenario by emission source
emit_efp_2100_forbar = pd.concat([elec_emissions_cumu.loc[2100],fuel_emissions_cumu.loc[2100],process_emissions_cumu.loc[2100]],
                                 axis=1,keys=['electricity','fuel','process'])
In [254]:
emit_efp_2100_mmt = emit_efp_2100_forbar/1e12
In [255]:
fig_cumulativeemit, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(emit_efp_2100_mmt.index[0:5], emit_efp_2100_mmt['process'].iloc[0:5],label='process',
        tick_label=scennames_labels[0:5], color='black')

ax0.bar(emit_efp_2100_mmt.index[0:5], emit_efp_2100_mmt['fuel'].iloc[0:5],label='fuel',
        bottom=emit_efp_2100_mmt['process'].iloc[0:5], 
        tick_label=scennames_labels[0:5], color='darkred')

ax0.bar(emit_efp_2100_mmt.index[0:5], emit_efp_2100_mmt['electricity'].iloc[0:5],label='electricity',
        bottom=emit_efp_2100_mmt['process'].iloc[0:5]+emit_efp_2100_mmt['fuel'].iloc[0:5],
        tick_label=scennames_labels[0:5], color='blue')

ax0.set_ylim(0,maxy+1000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes $CO_{2eq}$]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.6', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(emit_efp_2100_mmt.index[5:8], emit_efp_2100_mmt['process'].iloc[5:8],
        tick_label=scennames_labels[5:8], color='black')

ax2.bar(emit_efp_2100_mmt.index[5:8], emit_efp_2100_mmt['fuel'].iloc[5:8],
        bottom=emit_efp_2100_mmt['process'].iloc[5:8], 
        tick_label=scennames_labels[5:8], color='darkred')

ax2.bar(emit_efp_2100_mmt.index[5:8], emit_efp_2100_mmt['electricity'].iloc[5:8],
        bottom=emit_efp_2100_mmt['process'].iloc[5:8]+emit_efp_2100_mmt['fuel'].iloc[5:8],
        tick_label=scennames_labels[5:8], color='blue')

ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.6', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(emit_efp_2100_mmt.index[8:], emit_efp_2100_mmt['process'].iloc[8:],
        tick_label=scennames_labels[8:], color='black')

ax3.bar(emit_efp_2100_mmt.index[8:], emit_efp_2100_mmt['fuel'].iloc[8:],
        bottom=emit_efp_2100_mmt['process'].iloc[8:], 
        tick_label=scennames_labels[8:], color='darkred')

ax3.bar(emit_efp_2100_mmt.index[8:], emit_efp_2100_mmt['electricity'].iloc[8:],
        bottom=emit_efp_2100_mmt['process'].iloc[8:]+emit_efp_2100_mmt['fuel'].iloc[8:],
        tick_label=scennames_labels[8:], color='blue')

ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.6', ls='--') 
ax3.set_axisbelow(True)

#overall fig
fig_cumulativeemit.suptitle('Cumulative Emisisons in 2100 by emission source', fontsize=24)
handles, labels = plt.gca().get_legend_handles_labels()
plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.42,1))
plt.show()

#fig_eBalance.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')
In [ ]:

vMFG versus Circular Processes¶

3 categories for this:

  • vmfg of materials
  • CE of materials and materials
  • install/decomission requirements and I think module mfging falls into the last category
In [256]:
CEkey = ['ReMFG','Recycle_Crush','Recycled_LQ','Recycled_HQ','Resell','Repair','LQ','HQ']
CEkey_search = '|'.join(CEkey)

LinearKey = ['vMFG', 'vmfg', 'landfill']
LinearKey_search = '|'.join(LinearKey)

otherkey = ['mod_MFG','OandM','Install','Demount', 'Store'] #THIS ONE IS MISSING COLUMNS!!!
otherkey_search = '|'.join(otherkey)

scen_carbon_CE.columns

scen_carbon_linear.columns

scen_carbon_other.columns

allcolumns = list(scen_annual_carbon.columns) selectedcolumns = list(scen_carbon_other.columns)+list(scen_carbon_linear.columns)+list(scen_carbon_CE.columns) selectedcolumns

set(allcolumns).difference(selectedcolumns)

In [257]:
#subset by pathways
sim_carbon_CE = pd.DataFrame()
sim_carbon_linear = pd.DataFrame()
sim_carbon_necessary = pd.DataFrame()

for scen in scenarios:
    scen_annual_carbon = sim_annual_carbon.filter(like=scen)
    
    scen_carbon_CE = scen_annual_carbon.loc[:,scen_annual_carbon.columns.str.contains(CEkey_search)]
    scen_carbon_CE_sum = scen_carbon_CE.sum(axis=1)
    sim_carbon_CE = pd.concat([sim_carbon_CE, scen_carbon_CE_sum], axis=1)
    
    scen_carbon_linear = scen_annual_carbon.loc[:,scen_annual_carbon.columns.str.contains(LinearKey_search)]
    scen_carbon_linear_sum = scen_carbon_linear.sum(axis=1)
    sim_carbon_linear = pd.concat([sim_carbon_linear,scen_carbon_linear_sum ], axis=1)
    
    scen_carbon_other = scen_annual_carbon.loc[:,scen_annual_carbon.columns.str.contains(otherkey_search)]
    scen_carbon_other_sum = scen_carbon_other.sum(axis=1)
    sim_carbon_necessary = pd.concat([sim_carbon_necessary, scen_carbon_other_sum], axis=1)
    
sim_carbon_CE.columns = scennames_labels_flat
sim_carbon_linear.columns = scennames_labels_flat
sim_carbon_necessary.columns = scennames_labels_flat

#.index = pd.RangeIndex(start=2000,stop=2101,step=1)
In [258]:
sim_carbon_CE_cumu = sim_carbon_CE.cumsum()
sim_carbon_linear_cumu = sim_carbon_linear.cumsum()
sim_carbon_necessary_cumu = sim_carbon_necessary.cumsum()
In [259]:
emit_pathway = pd.concat([sim_carbon_CE_cumu.loc[2100],sim_carbon_linear_cumu.loc[2100],sim_carbon_necessary_cumu.loc[2100]],
                                 axis=1,keys=['Circular','Linear/Virgin','Necessary'])
emit_pathway_mmt = emit_pathway/1e12
In [260]:
sim_carbon_CE_cumu
Out[260]:
PV_ICE PERC SHJ TOPCon Low Quality Long-Lived High Eff Circular High Eff + Long-life Long-Life + Recycling Recycled-Si + Long-life Circular + Long-life Circular + High Eff
2000 1.243800e+08 1.133821e+08 1.104993e+08 1.119274e+08 1.243800e+08 1.186951e+08 1.102168e+08 1.243800e+08 1.102168e+08 1.186951e+08 1.243800e+08 1.243800e+08 1.243800e+08
2001 1.671031e+08 1.560264e+08 1.531229e+08 1.545612e+08 1.671031e+08 1.613775e+08 1.528384e+08 1.671031e+08 1.528384e+08 1.613775e+08 1.671031e+08 1.671031e+08 1.671031e+08
2002 2.168491e+08 2.025179e+08 1.987614e+08 2.006223e+08 2.168491e+08 2.094412e+08 1.983933e+08 2.168491e+08 1.983933e+08 2.094412e+08 2.168491e+08 2.168491e+08 2.168491e+08
2003 2.898076e+08 2.707032e+08 2.656955e+08 2.681761e+08 2.898039e+08 2.799324e+08 2.652048e+08 2.898076e+08 2.652048e+08 2.799324e+08 2.898076e+08 2.898076e+08 2.898076e+08
2004 4.327494e+08 4.042926e+08 3.968334e+08 4.005285e+08 4.327160e+08 4.180398e+08 3.961025e+08 4.327494e+08 3.961025e+08 4.180398e+08 4.327494e+08 4.327494e+08 4.327494e+08
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2096 9.670630e+12 1.573488e+15 1.496899e+15 1.528699e+15 3.048649e+15 4.351918e+10 4.124697e+10 1.931687e+15 4.124697e+10 2.468899e+14 2.274649e+15 1.053526e+15 1.149886e+15
2097 9.812793e+12 1.603712e+15 1.525642e+15 1.558055e+15 3.115547e+15 4.351918e+10 4.124697e+10 1.957287e+15 4.124697e+10 2.588737e+14 2.342792e+15 1.061050e+15 1.168394e+15
2098 9.971351e+12 1.615818e+15 1.537155e+15 1.569813e+15 3.180545e+15 4.351918e+10 4.124697e+10 1.983421e+15 4.124697e+10 2.704718e+14 2.410676e+15 1.068903e+15 1.187402e+15
2099 1.014798e+13 1.628246e+15 1.548973e+15 1.581884e+15 3.243294e+15 4.351918e+10 4.124697e+10 2.009839e+15 4.124697e+10 2.816133e+14 2.479532e+15 1.077202e+15 1.206669e+15
2100 1.034754e+13 1.641126e+15 1.561223e+15 1.594394e+15 3.304882e+15 4.351918e+10 4.124697e+10 2.036589e+15 4.124697e+10 2.922333e+14 2.549814e+15 1.096187e+15 1.226242e+15

101 rows × 13 columns

In [261]:
#emit_pathway.sum(axis=1) #check that matches, we're good
emit_pathway_mmt
Out[261]:
Circular Linear/Virgin Necessary
PV_ICE 10.347537 31915.729680 102.295652
PERC 1641.125975 22389.535967 93.838061
SHJ 1561.222574 21315.545872 89.336444
TOPCon 1594.394473 21765.176241 91.204095
Low Quality 3304.881583 32303.245880 119.952898
Long-Lived 0.043519 25430.432477 95.722591
High Eff 0.041247 32721.577893 76.414061
Circular 2036.589192 27770.494768 146.390627
High Eff + Long-life 0.041247 24777.586067 80.991964
Long-Life + Recycling 292.233275 25065.419312 96.297185
Recycled-Si + Long-life 2549.814266 23683.755176 356.503674
Circular + Long-life 1096.187470 28182.485670 137.917844
Circular + High Eff 1226.242395 24540.416674 124.775728
In [262]:
fig_emitByPathway, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(emit_pathway_mmt.index[0:5], emit_pathway_mmt['Necessary'].iloc[0:5],label='Necessary',
        tick_label=scennames_labels[0:5], color='darkgray', edgecolor='black')

ax0.bar(emit_pathway_mmt.index[0:5], emit_pathway_mmt['Linear/Virgin'].iloc[0:5],label='Linear/Virgin',
        bottom=emit_pathway_mmt['Necessary'].iloc[0:5], 
        tick_label=scennames_labels[0:5], color='darkorange')

ax0.bar(emit_pathway_mmt.index[0:5], emit_pathway_mmt['Circular'].iloc[0:5],label='Circular',
        bottom=emit_pathway_mmt['Necessary'].iloc[0:5]+emit_pathway_mmt['Linear/Virgin'].iloc[0:5],
        tick_label=scennames_labels[0:5], color='green')

ax0.set_ylim(0,maxy+1000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes $CO_{2eq}$]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.6', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(emit_pathway_mmt.index[5:8], emit_pathway_mmt['Necessary'].iloc[5:8],label='Necessary',
        tick_label=scennames_labels[5:8], color='darkgray', edgecolor='black')

ax2.bar(emit_pathway_mmt.index[5:8], emit_pathway_mmt['Linear/Virgin'].iloc[5:8],label='Linear/Virgin',
        bottom=emit_pathway_mmt['Necessary'].iloc[5:8], 
        tick_label=scennames_labels[5:8], color='darkorange')

ax2.bar(emit_pathway_mmt.index[5:8], emit_pathway_mmt['Circular'].iloc[5:8],label='Circular',
        bottom=emit_pathway_mmt['Necessary'].iloc[5:8]+emit_pathway_mmt['Linear/Virgin'].iloc[5:8],
        tick_label=scennames_labels[5:8], color='green')

ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.6', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(emit_pathway_mmt.index[8:], emit_pathway_mmt['Necessary'].iloc[8:],label='Necessary',
        tick_label=scennames_labels[8:], color='darkgray', edgecolor='black')

ax3.bar(emit_pathway_mmt.index[8:], emit_pathway_mmt['Linear/Virgin'].iloc[8:],label='Linear/Virgin',
        bottom=emit_pathway_mmt['Necessary'].iloc[8:], 
        tick_label=scennames_labels[8:], color='darkorange')

ax3.bar(emit_pathway_mmt.index[8:], emit_pathway_mmt['Circular'].iloc[8:],label='Circular',
        bottom=emit_pathway_mmt['Necessary'].iloc[8:]+emit_pathway_mmt['Linear/Virgin'].iloc[8:],
        tick_label=scennames_labels[8:], color='green')

ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.6', ls='--') 
ax3.set_axisbelow(True)

#overall fig
fig_emitByPathway.suptitle('Cumulative Emissions in 2100 by CE Category', fontsize=24)
handles, labels = plt.gca().get_legend_handles_labels()
plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.5,1))
plt.show()

#fig_eBalance.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')
In [ ]:
In [ ]:

Emissions per Capacity¶

Trying to quantify the emissions entailed in achieving energy transition target capacities. Our current calculations don't allow a good method of CO2/kWh, but we do know how much it now takes to achieve the first 75 TW then the the next 11 TW will entail a different amount of carbon. This may be a valuable comparison

In [263]:
cumu_carbon_rankings_crittime#.loc[scen,2050]
Out[263]:
2050 2100
PV_ICE 15334.402913 32028.372868
PERC 13931.390546 24124.500003
SHJ 13271.021411 22966.104890
TOPCon 13548.838219 23450.774808
Low Quality 16784.129725 35728.080361
Long-Lived 14549.710638 25526.198587
High Eff 11907.894289 32798.033201
Circular 19264.136355 29953.474587
High Eff + Long-life 12273.873646 24858.619278
Long-Life + Recycling 14585.197527 25453.949771
Recycled-Si + Long-life 15795.409039 26590.073117
Circular + Long-life 20069.816625 29416.590984
Circular + High Eff 16877.271493 25891.434797
In [264]:
cumu_carbon_rankings_crittime.index = scenarios #relabel the index for the calc
scen_carbonPERcapacity = pd.DataFrame(index=scenarios)
for scen in scenarios:
    #2050
    scen_effectiveCap_TW = sim1.scenario[scen].dataOut_m.loc[50, 'Effective_Capacity_[W]']/1e12
    scen_carbon_mmt = cumu_carbon_rankings_crittime.loc[scen,2050]
    scen_carbonPERcapacity.loc[scen, 'EffectiveCap_TW_2050'] = scen_effectiveCap_TW
    scen_carbonPERcapacity.loc[scen, 'Carbon_mmt_2050'] = scen_carbon_mmt
    scen_carbonPERcapacity.loc[scen, 'CO2pTW_2050'] = scen_carbon_mmt/scen_effectiveCap_TW
    #2100
    scen_effectiveCap_TW_2100 = sim1.scenario[scen].dataOut_m.loc[100, 'Effective_Capacity_[W]']/1e12
    scen_carbon_mmt_2100 = cumu_carbon_rankings_crittime.loc[scen,2100]
    scen_carbonPERcapacity.loc[scen, 'EffectiveCap_TW_2100'] = scen_effectiveCap_TW_2100
    scen_carbonPERcapacity.loc[scen, 'Carbon_mmt_2100'] = scen_carbon_mmt_2100
    scen_carbonPERcapacity.loc[scen, 'CO2pTW_2100'] = scen_carbon_mmt_2100/scen_effectiveCap_TW_2100
    #marginal increase between 2050 and 2100
In [265]:
round(scen_carbonPERcapacity,0)
Out[265]:
EffectiveCap_TW_2050 Carbon_mmt_2050 CO2pTW_2050 EffectiveCap_TW_2100 Carbon_mmt_2100 CO2pTW_2100
PV_ICE 75.0 15334.0 204.0 86.0 32028.0 373.0
r_PERC 75.0 13931.0 186.0 86.0 24125.0 281.0
r_SHJ 75.0 13271.0 177.0 86.0 22966.0 267.0
r_TOPCon 75.0 13549.0 181.0 86.0 23451.0 273.0
r_IRENA 75.0 16784.0 224.0 86.0 35728.0 416.0
ex_Life 75.0 14550.0 194.0 86.0 25526.0 297.0
ex_High_eff 75.0 11908.0 159.0 86.0 32798.0 382.0
ex_Circular 75.0 19264.0 257.0 86.0 29953.0 348.0
h_EffLife 75.0 12274.0 164.0 86.0 24859.0 289.0
h_50PERC 75.0 14585.0 194.0 86.0 25454.0 296.0
h_RecycledPERC 75.0 15795.0 211.0 86.0 26590.0 309.0
h_Perovskite_life 75.0 20070.0 268.0 86.0 29417.0 342.0
h_Perovskite_Eff 75.0 16877.0 225.0 86.0 25891.0 301.0
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